Resonator

ABSTRACT

At least one resonator is disclosed having a plurality of nanoscale resonator elements, the at least one resonator having at least two, different resonant frequencies and configured to provide at least two signals in response to an input signal and at least two adders configured to weight the signals with respective weights and to add weighted signals so as to produce an output signal.

BACKGROUND OF THE INVENTION

1. Technical Field

The present disclosure relates to resonator(s).

2. Discussion of Related Art

Some forms of signal processing, such as pattern recognition, datamining and sensor signal processing, involve classifying or categorizingdata.

Classifying and categorizing data has been the subject of intensiveresearch for several decades. For example, an audio pattern recognitionbased on resonators is described on page 146 of “Self-Organisation andAssociative Memory” by Teuvo Kohonen (Springer, 1984).

It has been proposed to implement pattern recognition in hardware.

In recent years, machine learning algorithms have evolved forclassifying data. These algorithms tend to use digital signal processorsand employ mathematical methods based on statistical methods andoptimization processes.

An example of classifying data will now be described.

A chemical sensor system or “artificial nose” can be used to identify anodor by measuring concentrations of n different chemicals. The result ofa measurement is an n-dimensional vector of measurement values.Recognizing a particular odor involves determining if the n-dimensionalvector belongs to a specific cluster of points in n-dimensional space.The system learns to classify these points using certain mathematicalrules known as “discriminant functions” which divide n-dimensional spaceinto decision regions.

FIG. 1 illustrates a simple example of a two-dimensional space 1 inwhich data values 2 are classified into three groups 3 by threediscriminant functions 4. Discrimination can be carried out based on amethod using a form of discriminant known as a support vector machine(SVM). A discriminant, g(x), is defined in terms of a set of supportvectors, α^(t), and a non-linear Kernel function K(x^(t),x), namely:

$\begin{matrix}{{g(x)} = {\sum\limits_{t}\; {\alpha^{t}r^{t}{K\left( {x^{t},x} \right)}}}} & (1)\end{matrix}$

and where, in this case, a Gaussian radial basis Kernel functionK(x^(t),x) is used, namely:

$\begin{matrix}{{K\left( {x^{t},x} \right)} = {\exp \left\lbrack {- \frac{{{x^{t} - x}}^{2}}{\sigma^{2}}} \right\rbrack}} & (2)\end{matrix}$

The Kernel function is typically calculated using a digital signalprocessor using a multiplication unit.

It may be useful for portable devices, e.g. handheld devices orsmaller-sized devices, to classify or categorize data. However, thesetypes of devices may have limited-capacity power sources and/or limitedcomputing resources.

SUMMARY OF CERTAIN EMBODIMENTS OF THE INVENTION

According to a first aspect of certain embodiments of the presentinvention there is provided apparatus comprising at least one resonatorcomprising a plurality of nanoscale resonator elements, having at leasttwo, different resonant frequencies and being configured to provide atleast two signals in response to an input signal, the apparatuscomprising at least one adder configured to weight the signals withrespective weights and to add weighted signals so as to produce anoutput signal.

Thus, the apparatus can be used to implement, in the form of transferfunctions, Kernal functions of a support vector machine for classifyingdata and so can be used to classify data by analog data processing whichcan be more efficient than digital signal processing.

The input signal may be frequency coded and/or may have normalizedamplitude. The response signals may be amplitude coded. The input signalmay be relatively high frequency and the response signal may berelatively low frequency.

The at least one resonator may comprise a resonator comprising theplurality of nanoscale resonator elements and different parts of theresonator have different resonant frequencies. The resonator maycomprise an array of nanoscale resonator elements. The apparatus maycomprise at least two bandpass filters configured to extract the atleast two signals from an aggregate signal.

The apparatus may comprise at least two resonators, each resonatorcomprising a plurality of nanoscale resonator elements and eachresonator being configured to provide a signal in response to the inputsignal.

The plurality of nanoscale resonator elements may comprise a pluralityof nanowires. The plurality of nanoscale resonator elements may comprisea piezoelectric material, such as zinc oxide or barium titanate.

The plurality of nanoscale resonator elements may comprise a pluralityof nanotubes upstanding from a base. The nanotubes may comprise carbonnanotubes.

The plurality of nanoscale resonators may comprise a plurality oftwo-dimensional conductive sheets, which may comprise graphene.

The or each respective resonator may have a natural resonant frequencyand variance. At least one resonant frequency and/or variance may beprogrammable. The apparatus may comprise a gate configured to apply anelectric field to a resonator so as to program the resonant frequencyand/or variance. The apparatus may comprise a heater configured to causechange in phase of at least some of the nanoscale resonator elements soas to program the resonant frequency and/or variance.

The apparatus may comprise at least two transmission lines, eachtransmission line coupled to a respective resonator. The at least tworesonators may be configured to receive the same input signal.

The apparatus may further comprise at least one multiplier, eachrespective multiplier configured to combine signals from at least tworesonators and to provide a combined signal to an adder. The multipliermay be a diode multiplier. Each adder may comprise a programmablejunction. Each respective adder may comprise a junction between twoconductive lines, such as nanowires. The junction may be configured tohave a value of coupling constant which is continuously variable. Thejunction may be configured to have a value of coupling constant which isswitchable between at least two discrete values. The junction mayinclude functional molecules. Each adder may comprise of a programmablevariable resistor. The values of the or each resistor may determine arespective weight for an adding operation.

According to a second aspect of certain embodiments of the presentinvention there provided a module comprising at least one input terminalfor receiving at least one respective input signal and the apparatus,the apparatus configured to receive the at least one input signal and tooutput at least one signal classifying the at least one respective inputsignal.

According to a third aspect of certain embodiments of the presentinvention there is provided a device comprising a circuit configured toprovide a frequency coded signal and a module configured to receive thefrequency coded signal and to output a signal classifying the frequencycoded signal.

According to a fourth aspect of certain embodiments of the presentinvention there is provided apparatus comprising a digital processor, aclassifier comprising the apparatus and at least one input signal sourceconfigured to at least one input signal to the classifier, wherein theclassifier is configured to pass an output to the digital processor.

The digital processor may be configured to determine at least oneparameter for the classifier. The digital processor may be configured toconfigure the classifier in dependence upon the at least one classifier.

According to a fifth aspect of certain embodiments of the presentinvention there is provided apparatus comprising at least one resonatingmeans comprising a plurality of nanoscale resonating means, the at leastone resonating means having at least two, different resonant frequenciesand being configured to provide at least two signals in response to aninput signal, the apparatus comprising at least one adding meansconfigured to weight the signals with respective weights and to addweighted signals so as to produce an output signal.

According to a sixth aspect of certain embodiments of the presentinvention there is provided a method classifying an input signal usingat least one resonator comprising a plurality of nanoscale resonatorelements having at least two, different resonant frequencies, weightingthe signals with respective weights and adding weighted signals so as toproduce an output signal.

The method may further comprise measuring a temperature of a resonatorand providing a signal dependent upon the temperature to the resonator

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described, by way ofexample, with reference to the accompanying drawings in which:

FIG. 1 illustrates decision regions in two-dimensional space;

FIG. 2 is a schematic block diagram of apparatus for classifying data inaccordance with certain embodiments of the present invention;

FIG. 3 is a schematic block diagram of a classifier for classifying datain accordance with certain embodiments of the present invention;

FIG. 4 illustrates a so-called “hidden layer” of a classifier inaccordance with certain embodiments of the present invention;

FIG. 5 illustrates an example of implementing all or part of the hiddenlayer shown in FIG. 4;

FIG. 6 illustrates another example of implementing all or part thehidden layer shown in FIG. 4;

FIG. 7 illustrates a transfer function of the hidden layer shown in FIG.5;

FIG. 8 illustrates a simulation of an ensemble of nanoscale resonators;

FIG. 9 is a schematic diagram of apparatus for classifying data receivedfrom one signal source in accordance with certain embodiments of thepresent invention;

FIG. 10 illustrates frequency components of an input signal;

FIG. 11 shows identifying a radio context using the apparatus shown inFIG. 10;

FIG. 12 illustrates an output signal from the apparatus shown in FIG.10;

FIG. 13 is a schematic diagram of apparatus for classifying datareceived from two signal sources in accordance with some embodiments ofthe present invention;

FIG. 14 illustrates a transfer function provided by two resonators inthe apparatus shown in FIG. 13;

FIG. 15 illustrates a two-dimensional space before classification usingthe apparatus shown in FIG. 13;

FIG. 16 illustrates a two-dimensional space after classification usingthe apparatus shown in FIG. 13;

FIG. 17 is a schematic block diagram of signal conditioning andprocessing circuit;

FIG. 18 is a more detailed view of a transmission line and signalconditioning and processing circuit shown in FIG. 17;

FIG. 19 is a perspective view of the transmission line and a nanoscaleresonator ensemble shown in FIG. 18;

FIG. 20 is a diode multiplier circuit;

FIG. 21 illustrates a simplified schematic view of the structure ofapparatus for classifying data received from one signal source inaccordance with certain embodiments of the present invention;

FIG. 22 illustrates a simplified schematic view of the structure ofapparatus for classifying data received from two signal sources inaccordance with certain embodiments of the present invention;

FIG. 23 is a schematic diagram of apparatus for classifying datareceived a temperature sensor and one or more other types of sensor inaccordance with some embodiments of the present invention;

FIG. 24 is a schematic diagram of apparatus for classifying data usingone resonator in accordance with certain embodiments of the presentinvention; and

FIG. 25 illustrates a portable device including apparatus forclassifying data in accordance with certain embodiments of the presentinvention.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

Referring to FIG. 2, apparatus 5 for classifying data in accordance withcertain embodiments of the present invention is shown.

The apparatus 5 includes an array of one or more sensors 6 for providingraw and/or static or quasi-static signals 7, and/or time-varying signals8 with normalized amplitudes, an optional signal converter 9 forconverting non-normalized and/or (quasi-)static signals 7 intonormalized, time-varying signals 8, a classifier 10 classifying thesignals 8 and producing classification data 11 and a digital signalprocessor 12 which produces an output signal 13. The processor 12 mayalso output one or more parameters 14 which can be fed back into theclassifier 10. All, some or none of the sensors 6 may producenormalized, time-varying signals 8. A signal converter 9 may beincorporated into a sensor 6.

The classifier 10 is a support vector machine (SVM) which uses ahypothesis space of linear functions in a high dimensional space to finddiscriminant functions. The classifier 10 can be trained, for example bythe processor 12, with an optimizing, learning algorithm to implement alearning bias.

Referring to FIG. 3, the classifier 10 comprises an input layer 15providing sensor signals, a higher-dimensional so-called “hidden layer”16 which creates a higher-dimensional hypothesis space and produces aplurality of responses 17, an output layer 18 which combines theresponses 17 using weights and produces an output 19, and a bias 20 totrain weights.

In this example, the input layer 15 includes the sensors 6. However, insome examples, the input layer 15 does not include the sensors 6 and theinput layer 15 may, for example, simply serve as an interface. In someembodiments, the input layer 15 provides the signal converter(s) 9.

The hidden layer 16 can take a signal 8 which is frequency coded at arelatively high frequencies and which has normalized amplitude andoutput a response 17 which is amplitude coded and which has a relativelylow frequency or frequencies. For example, the sensor 6 or the converter9 may output a frequency-modulated square wave of fixed amplitude. Thehidden layer 16 outputs a signal a low frequency which is characteristicof the events or environment of interest, such a changes in chemicalconcentration or changes in context.

Referring also to FIG. 4, the output, y(x), of the output layer 18 canbe defined as:

$\begin{matrix}{{y(x)} = {\sum\limits_{i = 1}^{n}\; {w_{i}{\varphi \left( {{x - c_{i}}} \right)}}}} & (3)\end{matrix}$

where n is the number of units 21 in the hidden layer 16 implementingnon-linear transfer functions, w_(i) are the weights of a summingoperation and φ is a non-linear function of Euclidean distance betweenan input vector x and a center function c_(i).

The n-dimensional hidden layer 16 which defines the non-linear functionsφ(∥x−c_(i)∥) can be implemented using an ensemble ofnanoelectromechanical resonator elements (or simply “nanoscale resonatorelement” or “resonant nanofeatures”) 22, such as an array of nanowires.The ensemble or array has a closely-separated group of elements whichinteract electrically and/or mechanically. The array may be ordered inone (or more) dimensions, for example, by being arranged on the same,e.g. planar, substrate. However, the array need not be periodic or havea period which changes in predefined way, e.g. steadily increasingperiodicity, in all or some dimensions.

Referring to FIGS. 5 and 6, the units 21 implementing the non-lineartransfer functions can be formed in the same array 23 or differentarrays 23. Arrays may be considered to be different, for example, ifthey are spaced sufficiently far apart, without intermediate resonatorelements, and do not substantially interact, e.g. where the arrays ofelements are separated by a distance, D, which is much greater (e.g. atleast one or two orders of magnitude) than the distance, d, betweenneighboring elements in an array. In some embodiments, some units 21 maybe implemented in the same array and other units 21 may be implementedusing other, different arrays 23.

Units 21 have different resonant frequencies. Units 21 can be formed indifferent parts of the array 23.

Referring also to FIG. 7, an ensemble of nanoelectromechanical resonatorelements 22 (FIG. 4) which are coupled electrically and/or mechanicalcan create a transfer function 25 T(ω) having more than one maximum 26.This arises due to non-linearities in electrical and/or mechanicalcoupling. Thus, a frequency response of a unit 21 depends on (and can bevaried by changing), for example, the dimensions, spacing and/orproperties of the nanoelectromechanical resonator elements 22.

The ensemble of the nanoelectromechanical resonators 22 can be arrangedto define mathematical functions for implementing machining learningalgorithms. In particular, in this example, the ensemble ofnanoelectromechanical resonators 22 implements a Gaussian Radial Basisfunction, namely

${\exp \left( {{- \frac{1}{2}}{{{x_{i} - x_{j}}}^{2}/\sigma^{2}}} \right)}.$

However, other transfer functions can be used as the function φ andinclude a linear function, i.e. x_(i)·x_(j), power function, i.e.(x_(i)·x_(j))^(d), a polynomial function, i.e. (ax_(i)·x_(j)+d)^(d), asigmoid function, i.e. tan h(ax_(i)·x_(j)+d) function.

The Gaussian Radial Basis function can be implemented using an ensemble23 of nanoscale mechanical resonators 22 that are coupled to externalinput signal and that are coupled to weakly together either mechanicallyor via electromagnetic interaction. If the ensemble consists ofindividual resonators that are distributed according to a nearlyGaussian distribution, the resonator ensemble synchronizes itself to acollective state. An array of weakly coupled resonators converges to anoscillatory phase-locked pattern, in other words, the oscillators tendto have the same oscillation frequency and constant, but not necessarilyequal phase.

Converting the amplitude of a collective resonating state of theensemble to a low frequency signal generates a mapping:

x=(x ₁ , . . . , x _(n))

Φ(x)=(φ₁(x), . . . , φ_(N)(x))  (4)

where x are frequency-based input signals and Φ(x) are amplitudes of theset of resonator ensembles. There can be more than one isolatedresonator ensembles or one resonator ensemble with multiple resonantstates. Thus, the hidden layer comprises a set of resonator ensemblesthat behave according to the Gaussian Radial Basis function:

$\begin{matrix}{{\varphi (x)} \approx {\exp\left( {- \frac{{{x - c_{j}}}^{2}}{\sigma^{2}}} \right)}} & (5)\end{matrix}$

Referring to FIG. 8, a simulated Gaussian Radial Basis function 27 basedon an ensemble of mechanical nanoscale resonator groups is shown. Thesimulation is based on five resonators having values of Q of 100 andhaving discretely Gaussian distributed resonance frequencies at fivedifferent resonant frequencies. FIG. 8 also shows an ideal Gaussianfunction 28 and responses 29 ₁, 29 ₂, 29 ₃, 29 ₄, 29 ₅ of individualresonators.

Summing amplitudes of individual ensembles can be implemented usinganalog complementary metal oxide semiconductor (CMOS) circuitry, digitalCMOS circuitry, nanowire arrays with variable connections betweencrossing wires and variable resistor networks.

Weights, w_(i), can be adjusted. The weights can be continuouslyvariable, stepwise variable or binary, i.e. on/off.

Equation (3) above specifies three parameters, namely a function centeror “central frequency” c_(i), the variance of Gaussian function σ_(i)and a weight w_(i).

Leaving aside the variance σ_(i), learning can be implemented in threedifferent ways, namely dynamically adjusting the weights w_(i) with theconstant function centers c_(i), dynamically adjusting the functioncenters c_(i) with the constant weights w_(i) or dynamically adjustingboth the function centers c_(i) and the weights w_(i).

Learning and/or teaching can be based on programming parameter valuesw_(i), c_(i) during the manufacturing and/or dynamically adjustingparameter values w_(i), c_(i) during use of the system.

Referring again to FIG. 2, the processor 12, for example in the form ofa CMOS-based digital signal processor, can be used to compute andoptimize the parameters 14 w_(i), c_(i), σ_(i) of the pre-processor,i.e. classifier 10.

This arrangement can have the advantage that calculation of parameters14, which can take up large amounts of computational and electricalpower, can be carried out relatively infrequently using a digital signalprocessor, whereas classification of sensor signals, which uses lesselectrical power, can be carried out more frequently using thepre-processor 10. This can help to reduce the overall energy and powerconsumption of the system.

The parameters 14 need not be computed by the processor 12, but can beimplemented by other learning mechanisms using, for example, nanoscalesystems. This may include the use of phase changing materials.

As will be explained later, it may not be possible to separate aninitial set of measurement values using a linear classifier, i.e. linearfunction for separating the data points (for example as shown in FIG.15). For this reason, the values of the system parameters w_(i), c_(i)can be optimized to arrange data points so that they are separable usinglinear classifier functions (for example as shown in FIG. 16).

Dynamic machine learning algorithms for training the system can beapplied as an overlaying structure that controls the system parametersw_(i) c_(i).

In an example which uses nanoscale resonator ensembles and crossbarjunctions, the resonator ensembles can be tuned by a bias voltage andthe values of crossbar junctions can be changed by an additionalelectrical signal. In another example which uses nanoscale resonatorensembles and CMOS circuitry, the value of weights based on variableresistor networks can be changed in different ways.

Teaching can be based on back propagation from analysis of the output ofthe system to the values of the controllable elements w_(i), c_(i).

Referring to FIG. 9, apparatus 30 for classifying data from one source 6which may be a sensor, a transducer, an antenna or some other form ofinput device which provides a time-varying signal 8 having one or morefrequency components in accordance with certain embodiments of thepresent invention is shown.

The signal 8 is frequency-modulated signal. However, other forms ofcoding can be used such as pulse-density coding. In certain embodiments,the signal 8 takes the form of an electrical signal. However, the signal8 can take the form of an optical, mechanical or thermal signal.

The signal 8 is fed into a set of transfer functions 21 implementingKernel functions, formed by one or more resonators 23. The or eachresonator 23 includes an array (herein also referred to as an“ensemble”) of weakly-coupled nanoscale resonator elements 22. Theresonator elements 22 have at least one dimension (e.g. width and/orthickness) which is less than about 1 μm, less than about 100 nm or lessthan about 10 nm. The resonator elements 22 are spaced apart fromnearest neighbor(s) by a separation which can be less than about 100 nm,less than about 10 nm or less than about 2 nm.

The resonators 23 can be nanoelectromechanical resonators formed fromnanowires, nanotubes, 2-dimensional sheets or other forms ofelectromechanical nanoscale resonator elements 22. The arrays may bearranged horizontally or vertically with respect to a planar base orsubstrate.

The resonator elements 22 can be formed from a semiconductor material,metal, metal alloy or metal oxide. The resonator elements 22 may bepiezoelectric.

The resonators 23 may be nanoscale optical resonators formed fromlocalized plasmonic resonator elements, quantum dot based resonatorelements or other forms of optical nanoscale resonator elements 22.

In relation to FIG. 9, for clarity, each function 21 is implemented byseparate arrays 23. However, more than one function 21 can beimplemented in the same array 23, in which case a reference to, forexample, different resonators 23 can be replaced by a reference todifferent parts of a resonator 23.

Each respective resonator 23 has a central resonant frequency, x_(mi),and the resonance frequencies of the resonator elements 22 in the sameresonator 23 are distributed in a continuous distribution around thecentral frequency, for example in Gaussian distribution. This can beused to provide an array of Gaussian transfer functions (or physicalmanifestations of Kernel functions) for signal processing. However,other distributions can be used, for example distributions which are notsymmetrical.

Each resonator 23 produces a response signal 17 which is proportional,e.g. linearly proportional, to the average amplitude of oscillation ofthe nanoscale resonator elements. In the case that more than onefunction 21 is implemented in the same array 23, the response signals 17may be mixed in an aggregate signal 60 (FIG. 24), but can be extractedusing band-pass filters 61 (FIG. 24).

The resonator elements 22 have a quality factor, Q, which may be of theorder of 100 or 1000. However, the resonator elements 22 may have alower or a higher quality factor according to the frequency of operationand the required resolution.

For a Gaussian distribution of the resonance frequencies, the responsesignal 17 for a i-th transfer function 21 can be expressed as z_(i),where:

$\begin{matrix}{z_{i} = {\exp \left( {- \frac{{{x - {\overset{\_}{x}}_{mi}}}^{2}}{\sigma^{2}}} \right)}} & (6)\end{matrix}$

where x is the input signal 8, x _(mi) is the center of the transferfunction 21 and σ is Gaussian variance of the transfer function 21.

As will be explained in more detail later, x _(mi) usually correspondsto a resonant frequency of the transfer function 21. If the transferfunction 21 is provided by one resonator 23, then the resonant frequencyx_(mi) of the resonator 23 is used instead of the average x _(mi).However, if the transfer function 21 is provided by more than oneresonator 23, for example as shown in apparatus 30′ (FIG. 14), then anaverage resonant frequency x _(mi) of the resonators 23 can be used.

The response signals 17 are fed into a set of adders or summing elements31 in the output layer 18 (FIG. 3) which weight and sum weighted signalsto produce output signals 19. As will be explained in more detail later,the array can be implemented using a crossbar structure of nanowirescoupled to each other by junctions with variable coupling constants.

The output signal 19 for a j-th adder 31 can be expressed as y_(j),where:

y _(j) =w _(j1) z ₁ + . . . +w _(j3) z ₃  (7)

Thus, the apparatus 30 can provide an analog processor for signalprocessing and, in particular, can be used to classify a signal byimplementing the Kernel functions of a support vector machine.

The input device 6 may be a broadband antenna and the apparatus 30 maybe used for recognizing the context of a radio environment.

Referring also to FIG. 10, the signal 8 from the antenna 6 includesseveral frequency components 32 having different magnitudes. Magnitudecan be measured in terms of intensity, amplitude or power. For example,the signal 8 may be radio-frequency spectrum. A first set of frequencycomponents 32 ₁ may represent frequency components found in a homeenvironment and second set of frequency components 32 ₂ may representfrequency components found in a work environment. The frequencycomponents 32 may comprise components arising from, for example aBluetooth™ network, wireless local area network (WLAN) etc.

The signal 8 from the antenna 6 is fed into three transfer functions 21provided by three respective resonators 23. The resonators 23 haverespective values of resonant frequency, namely x_(m1), x_(m3) andx_(m3), and respective values of Gaussian variance, namely σ_(m1),σ_(m2) and σ_(m3). The resonators 23 convert the signal 8 into signals17 that are proportional to the amplitude of oscillation of theresonating elements 22 in the resonators 23.

Referring also to FIG. 11, the apparatus 30 resolves the signal 8 intoone of a number of different contexts 33, e.g. home or work environment,by classifying the frequency components 32 using a discriminant function34.

Referring also to FIG. 12, the signal 8 can be resolved into a value 35,e.g. voltage, which can be compared with a threshold value 36 and soidentify the context.

The signal 8 need not be frequency coded, but can be coded in otherways. For example, the signal 8 can be pulse density coded, employ spikecoding or be based on analog voltage. For example, a voltage may beconverted by signal converter 9 (FIG. 2) into a frequency. This allowsfrequency signals to be synthesized. As will be explained later, whenusing multiple sensors, this technique can be used to combine signalsfrom different types of sensors.

Radio sensing using the apparatus 30, particularly using piezoelectricnanowires, can have advantages. For example, power consumption can below compared with conventional processor-based circuits. Radio sensingcan also occur in real time.

As explained earlier, the apparatus 30 may comprise one source 6 oftime-varying input signals 8 and so the transfer function 21 can beprovided by one resonator 23.

Referring to FIG. 13, a modified apparatus 30′ is shown with two sources6. The modified apparatus 30′ is the similar to the apparatus describedearlier and the same reference numerals are used to describe the samefeatures.

In this example, signals 8 from more than one source 6 can be fused intosignal analysis. This involves obtaining a value indicative of thesimilarity of each of the signals 8 to a resonator 23 and multiplyingthe similarity values.

Referring to FIG. 14, handling signals 8 from more than one source 6 canbe achieved by applying the signals 8 to respective resonators 23 (or toa resonator having more than one resonant mode), taking intermediateresponses 17′ from the resonators 23 (or extracting intermediateresponses using band pass filters) and multiplying the intermediateresponses 17′ using a multiplier 37 to produce the response 19. Thus,the transfer function produces a signal:

$\begin{matrix}{z_{i} = {\exp \left( {- \frac{{{\overset{\_}{x} - {\overset{\_}{x}}_{mi}}}^{2}}{\sigma^{2}}} \right)}} & \left. \left( 6’ \right. \right)\end{matrix}$

where x is an average of the two of more signals 8. As will be explainedin more detail later, the multiplier 37 is provided by a non-linearelement, such as a diode multiplier, and can be implemented, forexample, as a nanostructured silicon diode.

In the case that more than one transfer function 21 is implemented inthe same resonator array 23, different frequency intermediate responsescan be extracted using band-pass filters 61 (FIG. 24) and thenmultiplied as if they came from separate resonator arrays 23.

Multiple signal sources 6 allow a higher (i.e. n>2) dimensional vectorof measurement values to be classified.

Referring to FIGS. 15 and 16, the apparatus 30′ can receive signals fromtwo sources, such as a temperature sensor and a light intensity sensor,and can resolve pairs of measured signals 8, e.g. temperature and lightintensity, into one of a number of different contexts 38, e.g. at home,in an office or outdoors, by classifying measurement pairs 39 using twodiscriminant functions 40 ₁, 40 ₂.

There can be more than two signal sources. However, the apparatus 30 caneasily be modified to accommodate further signal sources 6 by providingan appropriate number of resonators 23 for each transfer function 21 (ora resonator 23 with an appropriate number of resonant modes) andmultiplying intermediate signals 17′ from the resonators 23 providing atransfer function 21. For example, if there are three sources 6, theneach transfer function 21 is provided by three resonators 23 (or aresonator 23 with three resonant modes).

In the examples described earlier, three transfer functions 21 areillustrated. However, these examples and other examples can use twotransfer functions or more than three transfer functions. Addingadditional transfer functions 21 allows further dimensions to beanalyzed.

Referring to FIG. 17, the apparatus 30 will now be described in moredetail.

The apparatus 30 includes an antenna or other signal source 6 and atransfer functions 21 formed of a nanoscale resonator ensemble 23. Atransmission line 41 feeds the input signal 8 from the sensor 6 to thenanoscale resonator ensemble 23. The signal from the nanoscale resonatorensemble 23 is fed into a high-pass filter 42 into a rectifier 43 and inturn is fed into a low pass filter 44. As shown in FIG. 17, the system30 can include more than one source 6. Not all the sources 6 need be ofthe same type.

Thus, the input signal 8 is fed into the nanoscale resonator ensemble 23which oscillates and the output is filtered to remove the low-frequencycomponent and the resulting signal is detected using the diode detectorwhich outputs a signal 17 which is subsequently used in summing or asignal 17′ which is subsequently used in multiplying.

Referring to FIGS. 18 and 19, the nanoscale resonator ensemble 23 can beincorporated into the transmission line 41. The transmission line 41includes a first strip 45 of conductive material, e.g. a highly dopedsemiconductor, such as silicon, and an overlying strip 46 of the same ordifferent conductive material separated by a layer 47 of dielectricmaterial, such as silicon dioxide. An annular space 48 is formed, e.g.by reactive ion etching, to define a central electrode 49. In theannular space 48, a nanoscale resonator ensemble 23 is formed comprisingnanoscale resonator elements 22 in the form of upstanding carbonnanotubes. In some embodiments, the nanoscale resonator elements 22 aresilicon nanowires. The nanoscale resonator elements 22 may be formed bydepositing a layer of metal, such as iron, which provides a catalyst andgrowing the nanoscale resonator elements by chemical vapor deposition.

The diameter of the central electrode 49 is about 1 to 100 μm. The widthof the annular space 48 is about 0.1 to 10 μm.

A bias voltage can be applied to the central electrode 49 to control theelectrical and/or mechanical properties of the resonator ensemble 23.

Resistors 50, 51 and capacitors 52, 53 forming the low-pass andhigh-pass filters 44, 42 are formed using doped semiconductor tracks.The diode 43 comprises a p-n junction formed, for example by implantingn-type impurity into a p-type substrate.

Referring to FIG. 20, output signals x₁, x₂ originating from differentnanoscale ensembles 23 can be multiplied using a diode multiplier 54comprising resistors 55 ₁, 55 ₂ which weight the signals x₁, x₂, arectifier 56 and low-pass filter 57.

Referring to FIGS. 21 and 22, output signals 17 are summed by crossingnanowires 58, 59 to form conductive and non-conductive junctions 60_(C), 60 _(NC). The resistance of the conductive junctions 60 _(C) canbe varied for example, by adsorbing organic material (e.g. functionalmolecules) onto the surface of the nanowires 58, 59. Selectiveabsorption or, conversely, desorption of material can be achieved bypassing current through the junction at high currents, i.e. to form or,conversely, blow a connection. As explained earlier, the weights can becontinuously variable, stepwise variable or binary, i.e. on/off.

Piezoelectric Nanoscale Resonator Elements

As explained earlier, the nanoscale resonator elements 22 may be formedfrom piezoelectric material. Piezoelectric nanowires can resonate in anapplied electric field and an array of piezoelectric nanowires canexhibit synchronized behavior. An array of weakly coupled resonatorsconverges on an oscillatory phase-locked pattern such that theresonators have the same oscillation frequency and constant, but notnecessarily equal, phase.

Thus, the amplitude of oscillation of the piezoelectric nanowires canvary according to the Gaussian distribution as a function of inputexcitation frequency. However, the piezoelectric nanowires converge tooscillate at the same frequency that equals to the input excitationfrequency. The array of piezoelectric nanowires converts the inputsignal into an amplitude that is a function of input signal frequencyand amplitude. If the input signal amplitude is normalized, then theoutput signal depends only on the input signal frequency. However, theinput signal need not be normalized.

Using piezoelectric nanowires instead of non-piezoelectric nanowires canhave advantages. For example, actuation of piezoelectric nanowires canbe more efficient than actuation of non-piezoelectric, capacitivelycoupled nanowires. Moreover, some piezoelectric nanowires, e.g. ZnOnanowires, can be grown at lower temperatures (e.g. about 70 to 100 orabout 400° C.) than some non-piezoelectric nanowires, such as carbonnanotubes. The thickness and length of some types of piezoelectricnanowires, such as ZnO nanowires, can be tightly controlled.Furthermore, compatibility of some types of piezoelectric nanowireswith, for example other fabrication processes can be better than sometypes of non-piezoelectric nanowires, such as carbon nanotubes.

Temperature Compensation

The characteristics of the resonators 23 can vary with temperature.However, temperature dependence can be compensated using an additionaltemperature sensor having a frequency output. The temperature signal canbe added to the classifier and thus it is possible to compensate fortemperature changes. The system can also learn the temperature behaviorand use the information as a part of the cognitive recognition process.

As explained earlier with reference to FIG. 13, signals from more thanone type of source can be incorporated into signal analysis.

Referring to FIG. 23, an example of the apparatus 30″ (FIG. 13)described earlier is shown which include a temperature sensor 6.

The temperature sensor 6 may be in the form of a resistor or diode whoseresistance depends on temperature. The signal from such a sensor isconverted into a time-varying signal using a signal converter 9 (FIG.2).

The temperature sensor 6 may be a resonator 22 comprising nanoscaleresonator elements 23 and which is fed back with a signal from theresonator 22 which may be amplified and phase shifted. Thus, the sensorcan be operated in a closed loop mode so that the resonant frequencydepends only on the temperature of the resonator 22 and thus the system.

The frequency coded, amplitude normalized signal of the temperaturesensor 6 is fed into the hidden layer 16, together with the signals 8 ofthe other sensor(s) 6.

The arrangement can allow temperature dependences of the sensor 6 andhidden layer elements 21 to be compensated.

Single Resonator

Referring to FIG. 24, apparatus 30″ for classifying data from one source6 using a single resonator 23 in the form of an array of nanoscaleresonating elements 22 in accordance with certain embodiments of thepresent invention is shown.

As explained earlier, more than one transfer function 21 havingdifferent resonant modes can be implemented in the same array 23. Forexample, by applying electric field(s) to different parts of the array23 and/or by introducing inhomogeneities or variations in dimensions,spacing or materials, an array 23 can exhibit more than one resonantfrequency with different parts, e.g. areas or volumes, of the same arrayresponding differently.

Thus, as shown in FIG. 24, an input signal 8 fed into the resonator 23can result in more than one response 17. Consequently, an overallresponse 60 of the array includes responses 17 from more than onetransfer function 21, i.e. the response 60 is an aggregate of more thanone response 17 and, possibly, other unwanted signals. To separate theresponses 17 from the aggregate signals 60, filters 61 can be used toselect each response. In this example, the filters 61 are band-passfilters centered on the resonant frequencies of the different parts ofthe array 23, e.g. f₁ and f₂. However, in some embodiments, a low passfilter and a high pass filter can additionally or alternatively be used.

Once separate responses 17 have been extracted, the responses 17 are fedinto a set of adders 31 which weight and sum the weighted signals toproduce output signals 19, as described earlier.

Portable Device

Referring to FIG. 25, a portable device 62 is shown.

The portable device 62 may be device which is usually held in one hand(“hand-held device”) such as mobile communications terminal, personaldigital assistant (PDA) or portable media player, a larger-sized device,such as a lap-top computer or other form of device which is usuallyplaced on a surface when operated by a user or a smaller-sized devicewhich need not be held by the user but can be worn, for example on theear or head, or is embedded in another article, such as another deviceor clothing.

The portable device 62 may have several functions. For example a mobilecommunications terminal may provide voice and data communicationfunctions via a public land mobile network (e.g. voice calling, textmessaging, e-mailing, web browsing etc via, e.g., a third-generationmobile network), voice and data communication functions via localnetwork (e.g. e-mailing, web browsing via, e.g. a wireless local areanetwork) and may also provide camera and media player functions.

The portable device 62 need not be a consumer item, such as mobilecommunications terminal, but can be an industrial item, such as an itemof testing or monitoring equipment.

The portable device 62 is powered by one or more limited-capacity powersource 63, such as a battery and/or photovoltaic cell.

The portable device 62 includes at least one sensor 6, for exampleinclude an antenna 6 ₁, classifier 10 and processor 12 and othercircuitry 64 providing appropriate functionality. For example, othercircuitry may include a microcontroller, volatile memory, non-volatilememory, an r.f. section, voice coder, display, user input devices (suchas touch screen, key pad, pointing device or multi-way controller), amicrophone, speaker(s), camera(s), GPS receiver, interfaces toperipheral devices or buses, a (U)SIM card reader and/or (U)SIM card.

It will be appreciated that many modifications may be made to theembodiments hereinbefore described without departing from the spirit andscope of the claimed invention.

1. Apparatus comprising: at least one resonator comprising a pluralityof nanoscale resonator elements, the at least one resonator having atleast two, different resonant frequencies and being configured toprovide at least two signals in response to an input signal; and atleast one adder configured to weight the signals with respective weightsand to add weighted signals so as to produce an output signal. 2.Apparatus according to claim 1, wherein the at least one resonatorcomprises a first resonator comprising the plurality of nanoscaleresonator elements and wherein different parts of the first resonatorhave different resonant frequencies.
 3. Apparatus according to claim 2,wherein the resonator comprises an array of nanoscale resonatorelements.
 4. Apparatus according to claim 1, comprising: at least twobandpass filters configured to extract the at least two signals from anaggregate signal.
 5. Apparatus according to claim 1, comprising: atleast two resonators, each resonator comprising a plurality of nanoscaleresonator elements and each resonator being configured to provide asignal in response to the input signal.
 6. Apparatus according to claim5, wherein the resonators comprises respective arrays of nanoscaleresonator elements.
 7. Apparatus according to claim 1, wherein theplurality of nanoscale resonator elements comprise a plurality ofnanowires.
 8. Apparatus according to claim 1, wherein the input signalis frequency coded, or has normalized amplitude, or both.
 9. Apparatusaccording to claim 1, wherein the input signal is relatively highfrequency and the responses are relatively low frequency.
 10. Apparatusaccording to claim 1, wherein the plurality of nanoscale resonatorelements comprise a piezoelectric material.
 11. Apparatus according toclaim 10, wherein the piezoelectric material comprises zinc oxide orbarium titanate.
 12. Apparatus according to claim 1, wherein theplurality of nanoscale resonator elements comprise a plurality ofnanotubes upstanding from a base and, optionally, wherein the nanotubescomprise carbon nanotubes.
 13. Apparatus according to claim 1, whereinthe plurality of nanoscale resonators comprise a plurality oftwo-dimensional conductive sheets and wherein, optionally, the pluralityof two-dimensional conductive sheets comprise graphene.
 14. Apparatusaccording to claim 1, wherein the resonator or each respective resonatorhas a natural resonant frequency and variance.
 15. Apparatus accordingto claim 14, wherein at least one resonant frequency, or variance, orboth is or are programmable.
 16. Apparatus according to claim 15,wherein the apparatus comprises: a gate configured to apply an electricfield to a resonator so as to program the resonant frequency, variance,or both.
 17. Apparatus according to claim 16, wherein the apparatuscomprises: a heater configured to cause change in phase of at least someof the nanoscale resonator elements so as to program the resonantfrequency, or variance, or both.
 18. Apparatus according to claim 5,comprising: at least two transmission lines, each transmission linecoupled to a respective resonator.
 19. Apparatus according to claim 5,wherein: the at least two resonators are configured to receive the sameinput signal.
 20. Apparatus according to claim 1, further comprising: atleast one multiplier, the at least one multiplier or each respectivemultiplier configured to combine signals and to provide a combinedsignal to an adder.
 21. Apparatus according to claim 20, wherein themultiplier is a diode multiplier.
 22. Apparatus according to claim 1,wherein each adder comprises a respective programmable junction. 23.Apparatus according to claim 1, wherein each respective adder comprisesa respective junction between two conductive lines.
 24. Apparatusaccording to claim 23, wherein the conductive lines comprise nanowires.25. Apparatus according to claim 23, wherein the junction is configuredto have a value of coupling constant which is continuously variable. 26.Apparatus according to claim 23, wherein the junction is configured tohave a value of coupling constant which is switchable between at leasttwo discrete values.
 27. Apparatus according to claim 23, wherein thejunction includes functional molecules.
 28. A module, comprising: atleast one input terminal for receiving at least one respective inputsignal; and the apparatus according to claim 1, the apparatus configuredto receive the at least one respective input signal and to output atleast one signal classifying the at least one respective input signal.29. A device comprising: a circuit configured to provide a frequencycoded signal; and a module according to claim 28 configured to receivethe frequency coded signal and to output a signal classifying thefrequency coded signal.
 30. Apparatus comprising: a digital processor; aclassifier comprising apparatus according to claim 1; and at least oneinput signal source configured to provide at least one input signal tothe classifier and the classifier configured to pass an output to thedigital processor.
 31. Apparatus according to claim 30 wherein thedigital processor is configured to determine at least one parameter forthe classifier.
 32. Apparatus according to claim 31 wherein the digitalprocessor is configured to configure the classifier in dependence uponthe at least one classifier.
 33. Apparatus comprising: at least oneresonating means comprising a plurality of nanoscale resonating means,the at least one resonating means having at least two, differentresonant frequencies and configured to provide at least two signals inresponse to an input signal; and at least one adding means configured toweight the signals with respective weights and to add weighted signalsso as to produce an output signal.
 34. A method comprising: classifyingan input signal using at least one resonator comprising a plurality ofnanoscale resonator elements having at least two, different resonantfrequencies; weighting the signals with respective weights; and addingweighted signals so as to produce an output signal.
 35. A methodaccording to claim 34 comprising: measuring a temperature of aresonator; and providing a signal dependent upon the temperature to theresonator.